Methylmercury production below the mixed layer in the North Pacific Ocean

Mercury enters marine food webs in the form of microbially generated monomethylmercury. Microbial methylation of inorganic mercury, generating monomethylmercury, is widespread in low-oxygen coastal sediments. The degree to which microbes also methylate mercury in the open ocean has remained uncertain, however. Here, we present measurements of the stable isotopic composition of mercury in nine species of marine fish that feed at different depths in the central North Pacific Subtropical Gyre. We document a systematic decline in δ 202 Hg, � 199 Hg and � 201 Hg values with the depth at which fish feed. We show that these mercury isotope trends can be explained only if monomethylmercury is produced below the surface mixed layer, including in the underlying oxygen minimum zone, that is, between 50 and more than 400 m depth. Specifically, we estimate that about 20–40 % of the monomethylmercury detected below the surface mixed layer originates from the surface and enters deeper waters either attached to sinking particles, or in zooplankton and micronekton that migrate to depth. We suggest that the remaining monomethylmercury found at depth is produced below the surface mixed layer by methylating microbes that live on sinking particles. We suggest that microbial production of monomethylmercury below the surface mixed later contributes significantly to anthropogenic mercury uptake into marine food webs. Mercury (Hg) is a globally distributed atmospheric pollutant that can form monomethyl-Hg (MMHg), which is neurotoxic and bioaccumulative in aquatic foo

Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. However, new algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad's ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism's transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data